|
|
|
|
|
|
Determination of Lead and Arsenic in Soil Samples by X Fluorescence Spectrum Combined With CARS Variables Screening Method |
JIANG Xiao-yu1, 2, LI Fu-sheng2*, WANG Qing-ya1, 2, LUO Jie3, HAO Jun1, 2, XU Mu-qiang1, 2 |
1. Engineering Research Center of Nuclear Technology Application, Ministry of Education, East China University of Technology, Nanchang 330013, China
2. State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China
3. Yangtze University,Wuhan 430000, China
|
|
|
Abstract As a quantitative analysis technique based on stoichiometry, X-ray fluorescence spectroscopy is very important to the prediction accuracy of the results. The competitive adaptive reweighted algorithm (CARS) adopted adaptive reweighted sampling technology and used interactive verification to select the lowest value square error (RMSECV) by interactive verification to find out the optimal combination of variables. To further improving the interpretation and prediction ability of PLS models, the competitive adaptive reweighted algorithm (CARS) was combined with X-ray fluorescence spectroscopy. A partial least square (PLS) model was established after screening the characteristic wavelength variables of lead and arsenic in the soil. Firstly, the CARS algorithm screened the wavelength variables closely related to lead content. When the sampling times were 26 times, 60 effective wavelength points were selected, and the wavelength variables closely related to arsenic content were screened. When the sampling times were 34 times, 19 effective wavelength points were selected. Then used the PLS method to establish the quantitative analysis model of lead and arsenic content in soil and compared it with the PLS model established by continuous projection algorithm (SPA) and Monte Carlo method. The results showed that the prediction sets Determination Coefficient (R2), Root Mean Square Error of Cross-Validation (RMSECV), Root Mean Square Error of Prediction (RMSEP) and Relative Prediction Deviation (RPD) of the lead CARS-PLS model were 0.995 5, 2.598 6, 3.228 and 9.401 1, respectively. Moreover, the prediction sets R2, RMSECV, RMSEP and RPD of arsenic CARS-PLS models were 0.999, 3.013 2, 2.737 1 and 8.211 6, respectively. The CARS-PLS model performance of the two elements is better than that of full-band PLS, SPA-PLS and MC-UVE-PLS model. The CARS-PLS algorithm based on the X fluorescence spectrum can effectively screen the characteristic wavelength, simplify the complexity of modeling, and improve the accuracy and robustness of the model.
|
Received: 2021-03-17
Accepted: 2021-06-06
|
|
Corresponding Authors:
LI Fu-sheng
E-mail: lifusheng@ecit.cn
|
|
[1] WEN Zhen-cai, SUN Tong, XU Peng, et al(温珍才, 孙 通, 许 朋,等). Journal of Jiangsu University·Natural Science Edition(江苏大学学报·自然科学版), 2015, 36(6): 673.
[2] REN Shun, ZHANG Xiong, REN Dong, et al(任 顺, 张 雄, 任 东,等). J. Instrum. Anal.(分析测试学报), 2020, 39(7): 829.
[3] BIN Jun, FAN Wei, ZHOU Ji-heng, et al(宾 俊, 范 伟, 周冀衡, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(1): 95.
[4] WU Xin-yan, BIAN Xi-hui, YANG Sheng, et al(武新燕, 卞希慧, 杨 盛,等). J. Instrum. Anal.(分析测试学报), 2020, 39(10): 1288.
[5] YU Lei, ZHU Ya-xing, HONG Yong-sheng,et al(于 雷, 朱亚星, 洪永胜,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(22): 138.
[6] JIANG Wei, FANG Jun-long, WANG Shu-wen, et al(姜 微, 房俊龙, 王树文,等). Journal of Northeast Agricultural University(东北农业大学学报), 2016, 47(2): 88.
[7] Li H D, Liang Y Z, Xu Q S, et al. Analytica Chimica Acta, 2009, 648: 77.
[8] Liang Y Z, Chen S, Zhang Z M. The Analyst, 2010, 135(5): 1138.
[9] Kennard R W, Stone L A. Technometrics, 1969, 11(1): 137.
[10] Centner V, Massart D L, de Noord O E, et al. Analytical Chemistry, 1996, 68(21): 3851.
[11] Araujo M C U, Saldanha T C B, Galvo R K H. Chemometrics and Intelligent Laboratory Systems, 2001, 57(2): 65.
[12] Galvao R K H, Araujo M C U, Fragoso W D. Chemometrics and Intelligent Laboratory Systems, 2008, 92(1): 83. |
[1] |
GUO Xiao-hua1, ZHAO Peng1, WU Ya-qing1, TANG Xue-ping3, GENG Di2*, WENG Lian-jin2*. Application of XRF and ICP-MS in Elements Content Determinations of Tieguanyin of Anxi and Hua’an County, Fujian Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3124-3129. |
[2] |
WANG Yi-ya1, WANG Yi-min1*, GAO Xin-hua2. The Evaluation of Literature and Its Metrological Statistics of X-Ray Fluorescence Spectrometry Analysis in China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1329-1338. |
[3] |
NI Zi-yue1, CHENG Da-wei2, LIU Ming-bo2, YUE Yuan-bo2, HU Xue-qiang2, CHEN Yu2, LI Xiao-jia1, 2*. The Detection of Mercury in Solutions After Thermal Desorption-
Enrichment by Energy Dispersive X-Ray Fluorescence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1117-1121. |
[4] |
CUI Ming-fang1, ZHU Jian-hua2*, HU Rui1, CHEN Shang-qian3. Research on the Chemical Composition and Process Feature of Ancient Porcelain Produced in Dongmendu Kiln[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 726-731. |
[5] |
YANG Jiong1, 2, QIU Zhi-li1, 4*, SUN Bo3, GU Xian-zi5, ZHANG Yue-feng1, GAO Ming-kui3, BAI Dong-zhou1, CHEN Ming-jia1. Nondestructive Testing and Origin Traceability of Serpentine Jade From Dawenkou Culture Based on p-FTIR and p-XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 446-453. |
[6] |
JIANG Yan1, MAO Ling-lin3, WU Jun3, YANG Xi4, DAI Lu-lu1, YANG Ming-xing1, 2*. Scientific Analysis of Five Turquoise Beads Unearthed From Haochuan Cemetery in Suichang, Zhejiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 568-574. |
[7] |
WANG Xue-yuan1, 2, 3, HE Jian-feng1, 2, 3*, NIE Feng-jun2, YUAN Zhao-lin1, 2, 3, LIU Lin1, 2, 3. Decomposition of X-Ray Fluorescence Overlapping Peaks Based on Quantum Genetic Algorithm With Multi-Fitness Function[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 152-157. |
[8] |
LIU Ji-fu1, YANG Ming-xing1*, SU Yue1, LIU Yue2. Analysis of Material and Source of Archaic Jade From the Tomb of Marquis Yi of Zeng in Suizhou, Hubei Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 215-221. |
[9] |
JIA Wen-bao1, TANG Xin-ru1, ZHANG Xin-lei1, SHAO Jin-fa2, XIONG Gen-chao1, LING Yong-sheng1, HEI Dai-qian3, SHAN Qing1*. Study on Sample Preparation Method of Plant Powder Samples for Total Reflection X-Ray Fluorescence Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3815-3821. |
[10] |
PENG Ya1,2, LI Dong-ling2,3*, WAN Wei-hao1,2, ZHOU Qing-qing3,4, CAI Wen-yi1,2, LI Fu-lin1, LIU Qing-bin2,3, WANG Hai-zhou2,3. Analysis of Composition Distribution of New Cast-Forging FGH4096 Alloy Turbine Disk Based on Microbeam X-Ray Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3498-3505. |
[11] |
YUE Su-wei1, 2, YAN Xiao-xu1, 2*, LIN Jia-qi1, WANG Pei-lian1, 2, LIU Jun-feng3. Spectroscopic Characteristics and Coloring Mechanism of Brown Tourmaline Under Heating Treatment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2524-2529. |
[12] |
WU Lian-hui1, 2, 3, HE Jian-feng1, 2, 3*, ZHOU Shi-rong2, 3, WANG Xue-yuan1, 2, YE Zhi-xiang2, 3. A Multi-Derivation-Spline Wavelet Analysis Method for Low Atomic Number Element EDXRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2530-2535. |
[13] |
ZOU Jin-ping1, ZHANG Shuai2, DONG Wen-tao2, ZHANG Hai-liang2*. Application of Hyperspectral Image to Detect the Content of Total Nitrogen in Fish Meat Volatile Base[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2586-2590. |
[14] |
BO Wei, LI Xiao-li*, DU Xue-miao, LIU Bin, ZHANG Qin, BAI Jin-feng. Investigation of a High-Pressure Pressed Powder Pellet Covered With Polyester Film Technique for the Determination of Chlorine in Soil and Sediment by X-Ray Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1828-1833. |
[15] |
LUO Li-qiang, SHEN Ya-ting. Advantages of X-Ray Spectrometry in Origin of Life, Earth Life on Earth and Global Climate Change[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 665-674. |
|
|
|
|